NEWS.md
In idiographic: Person-Specific (Idiographic) and Heterogeneous Complex Networks

idiographic 0.3.2

New package Title — "Idiographic Person-Specific and Heterogeneous Complex Networks" — and a rewritten Description with method references and .
Added Sonsoles López-Pernas as package author.
Documentation language standardized to British English (Language: en-GB), with dialect fixes across the documentation prose and a new inst/WORDLIST so the package spell check runs clean.
Slimmer CRAN footprint: all competitor-equivalence tests now live only in the repository's opt-in validation lane and are excluded from the CRAN tarball. Suggests trimmed from 16 to 8 packages — removed gimme, graphicalVAR, glasso, corpcor, data.table, qgraph, rio, and jsonlite, none of which the shipped package uses.

idiographic 0.2.0

Made the CRAN package offline-first: the only mandatory imports are standard R packages, while lme4, lavaan, plotting, and external backends are optional. Competitor-oracle tests and the real-panel corpus now run in a separate opt-in validation/ lane and are excluded from the CRAN tarball.
Added a registry-backed fit_idiographic() front door, estimator discovery, method-specific equivalence() declarations, package-wide equivalence_table() and argument-by-argument argument_coverage() ledgers, and common tidy accessors. All 17 registered methods and 315 current public formals now have an executable evidence classification; new unassessed arguments fail the closure test.
Expanded direct-oracle testing across graphicalVAR option combinations, mlVAR multi-lag/preprocessing/unique-model configurations, and bivariate plus three-variable GIMME standard, hybrid, and VAR searches. GIMME evidence now also covers fit statistics, uneven panels, exogenous-variable dimensions, and interacting correction/standardization controls. Tightened public argument validation so engine-specific controls cannot be silently ignored.
Closed the remaining executable evidence cells: all 12 supported lag-1 lmer mlVAR structure combinations, per-subject/missing-data graphicalVAR fits, GIMME 10.0 correction/stopping/standardization/cutoff/forced-path controls, standardized ML/MLR uSEM fits, Mplus wrapper forwarding/conversion, Bayesian burn-in/thinning, positive random-residual recovery, parallel mlVAR, and base-R linear/logistic idiographic-ML engine equality.
Migrated the 20-panel real ESM mlVAR validation corpus from the Dynalytics/psychaj work into the CRAN-excluded validation/ lane, with self-contained raw inputs, mlVAR 0.7.3 frozen oracles, provenance hashes, and explicit regression coverage for missing IDs, irregular occasion gaps, and degenerate between-person networks. Duplicate observation keys now fail clearly instead of producing row-order-dependent preprocessing.
Uniform fit_* naming for all estimators (breaking). Every model-fitting verb now uses a single fit_ prefix: fit_var(), fit_graphical_var(), fit_mlvar(), fit_rolling_var(), and so on for all estimators. Short model nicknames passed to compare_idiographic(), estimate_stability(), and validate_forecast() (for example, "var" and "graphical_var") are unchanged.
New native Bayesian estimators that statistically reproduce Mplus DSEM output without requiring Mplus:
fit_mlvar_bayes() — two-level Bayesian VAR(1) with latent mean centring. temporal = "fixed" is statistically validated against frozen Mplus DSEM fixed-temporal + random-intercept fixtures; temporal = "random" fits the full DSEM with person-specific temporal matrices and a random-effect covariance (reports random-slope SDs).
fit_var_bayes() — single-level Bayesian VAR(1), the unregularized Bayesian analogue of fit_graphical_var().
Pure-R conjugate Gibbs sampler (hand-rolled inverse-Wishart draws; no new dependencies). Posterior median / SD / 95% CI / one-tailed p, three networks (temporal, contemporaneous, between), and a Gelman-Rubin PSR diagnostic.
Validated to statistical (Monte-Carlo-error) equivalence against real Mplus 9 output with frozen ground-truth fixtures and parity tests.
Added fit_ml() for idiographic supervised machine-learning: ordered within-person train/test splits, person-specific models, pooled baselines on the same held-out rows, regression/classification metrics, row-level predictions, and coefficient extraction via coefs(). model names the statistical/ML model (for example, "ridge"), while estimator names the implementation/backend (default "native"). No new dependencies: native models include mean/majority baselines, OLS/logistic, ridge, lasso, elastic net, PCR, LDA, Gaussian naive Bayes, kNN, and one-split trees. fit_idiographic_ml() and fit_individualized_ml() remain aliases.

idiographic 0.1.0

Initial CRAN submission.
Provides idiographic network estimators for intensive longitudinal data, including ordinary VAR, graphical VAR, mlVAR, uSEM, and GIMME-style models.
Includes preprocessing audits, rolling-window estimation, forecast validation, edge stability diagnostics, model comparison, tidy accessors, and cograph plotting support.